Study says Artificial intelligence may improve kidney disease diagnosis

WASHINGTON DC: Researchers discovered that modern machine learning, a branch of artificial intelligence may augment the traditional way of diagnosing kidney disease. Pathologists often classify various kidney diseases on the basis of visual assessments of biopsies from patients' kidneys; however, machine learning has the potential to automate and augment the accuracy of classifications. In one study, a team led by Pinaki Sarder, PhD and Brandon Ginley, BS (Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo) developed a computational algorithm to detect the severity of diabetic kidney disease without human intervention. The algorithm examined a digital image of a patient's kidney biopsy at the microscopic level and extracted information on glomeruli, the small blood vessels of the kidney that filter waste from the blood for excretion. These structures are known to become progressively damaged and scarred over the course of diabetes, reported the study published in the journal -- journal of the American Society of Nephrology.